F.A.I.R. in AI: What It Covers, What It Misses, and How a Lifecycle View of Trust Adds Dimensions

Artificial Intelligence (AI) is becoming more advanced, spreading to areas like healthcare and finance.

As AI systems take on more responsibility, trust becomes very important. The F.A.I.R framework (Fairness, Accountability, Interpretability, Robustness) is popular for building trust in AI.

But it may miss the complex, multi-part nature of trust.

This article explores what F.A.I.R covers, what it misses, and how a lifecycle view of trust can add social, cultural, and ethical dimensions.

What F.A.I.R Covers

Fairness

Fairness in AI means removing biases in data and algorithms for fair decisions. It often defends against discrimination built unintentionally into AI.

Accountability

Accountability involves transparent decision-making through audit trails and documents. It ensures mistakes can be traced to specific decisions.

Interpretability

Also called explainability, this focuses on making the AI's decision process understandable. The clearer the "thinking," the more trustworthy it is.

Robustness

This ensures reliable performance under varying conditions, including adversarial attacks.

What F.A.I.R Misses

While comprehensive, F.A.I.R often misses socio-cultural aspects of trust like:

• Social Proof: Importance of endorsements from trusted entities.

• Historical Performance: Track record of reliable, ethical operation.

• Third-Party Audits: External assurance through scrutiny.

Adding a Lifecycle View of Trust

Like human trust, AI should meet diverse criteria to be trustworthy. Adding human trust signals provides social, cultural, and ethical dimensions:

• Social Proof

  • Example: AI systems in healthcare could include testimonials from doctors and patients who have had positive outcomes using the technology.

• Certifications by Independent Parties

  • Example: A financial AI system could gain trust by obtaining a cybersecurity certification from an independent auditor.

• Historical Performance

  • Example: An AI-driven customer service chatbot that has successfully resolved 98% of issues over the past year would be considered trustworthy.

• Reputation

  • Example: An AI system developed by a well-known university or ethical organization could leverage that reputation for trust.

• Third-Party Audits

  • Example: An AI system for law enforcement could allow for audits by civil rights organizations to ensure ethical data practices.

• Trial Periods and Demonstrations

  • Example: An AI-powered analytics tool could offer a 30-day free trial to allow users to assess its capabilities.

• Error Correction and Accountability

  • Example: An AI system that transparently reports its error rates and how those rates have declined over time can build trust through accountability.

Conclusion

Trust in AI is complex and multi-dimensional. While F.A.I.R provides a starting point, it needs other socially and culturally significant trust dimensions.

A more holistic view incorporating these can create AI systems that are technically proficient and resonate with human values.